title: “Bahn analysis report”

author: “Jinshi”

date: “March 26, 2019”

output:

html_document:

df_print: paged

1. The spatial destribution of global Rs sites

• We have much more measured Rs from the middle latitude region
• It is difficult to measure soil respiration all year around in the cold region
• Developed contries most located at Middle latitude region, thus recieved more funds to support the Rs measurements

Global spatial destribution of soil repiration sites

2. It is obvious that Rs measurements from cold region is more importent, but how?

Improve the Rs measure equipment so it can measure Rs in cold condition; Increasing funds; Bahn’s approach [Bahn et al. (2004) Biogeosciences] + Rs measured at annuam mean temperature linearly related with annual Rs rate + Rs at mean temperature: soil respiration measured at annual mean temperature / monthly mean temperature / daily mean temperature

3. The object of this analysis are

• Whether Rs measured at annuam mean temperature can represent annual Rs rate
• Whether Rs measured at monthly mean temperature can represent monthly mean Rs?

• Whether Rs measured at daily mean temperature represent daily mean Rs?

• When well represent and when not?

• If not what is the mechanism?

4. Results

4.1 Data used from SRDB_V4

• Climate space figure

## Warning: Removed 3 rows containing missing values (geom_point).

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## Warning: Removed 3 rows containing missing values (geom_point).

4.2.1 Overall results

4.2.2 Does TS_Source have effect?

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Using soil temperature

## 
## Call:
## lm(formula = bahn ~ Rs_annual, data = sdata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1269.9  -106.0    18.3   117.3  1222.3 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4.58745   17.22279  -0.266     0.79    
## Rs_annual    1.07455    0.01846  58.222   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 233.1 on 821 degrees of freedom
## Multiple R-squared:  0.805,  Adjusted R-squared:  0.8048 
## F-statistic:  3390 on 1 and 821 DF,  p-value: < 2.2e-16

## Tue Mar 26 18:09:52 2019  -------------------+++++-------------------
## Tue Mar 26 18:09:52 2019  How are Rs_annual and Rs_annual_bahn_Temp related?
## Tue Mar 26 18:09:52 2019  sdata rows = 823 cols = 143
## Tue Mar 26 18:09:52 2019  Model summary:
## 
## Call:
## lm(formula = temp ~ Rs_annual, data = sdata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1269.9  -106.0    18.3   117.3  1222.3 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4.58745   17.22279  -0.266     0.79    
## Rs_annual    1.07455    0.01846  58.222   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 233.1 on 821 degrees of freedom
## Multiple R-squared:  0.805,  Adjusted R-squared:  0.8048 
## F-statistic:  3390 on 1 and 821 DF,  p-value: < 2.2e-16
## 
## Tue Mar 26 18:09:52 2019  Plotting and saving model diagnostics...

## Tue Mar 26 18:09:52 2019  Plotting and saving model residuals...

## Tue Mar 26 18:09:52 2019  Saving outputs/3-modelresids.pdf

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## Tue Mar 26 18:09:52 2019  Test H0 of intercept=0: p-value = 0.7900293
## Tue Mar 26 18:09:52 2019  Test H0 of slope=1: p-value = 5.863901e-05

## [1] 0.7900293

## [1] 5.863901e-05

Using T_Annual

## 
## Call:
## lm(formula = bahn ~ Rs_annual, data = sdata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2450.0  -173.0    -0.3   151.8  4964.6 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -29.30007   27.53434  -1.064    0.288    
## Rs_annual     1.03037    0.02951  34.921   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 372.7 on 821 degrees of freedom
## Multiple R-squared:  0.5976, Adjusted R-squared:  0.5971 
## F-statistic:  1219 on 1 and 821 DF,  p-value: < 2.2e-16

## Tue Mar 26 18:09:53 2019  -------------------+++++-------------------
## Tue Mar 26 18:09:53 2019  How are Rs_annual and Rs_annual_bahn_Temp related?
## Tue Mar 26 18:09:53 2019  sdata rows = 823 cols = 143
## Tue Mar 26 18:09:53 2019  Model summary:
## 
## Call:
## lm(formula = temp ~ Rs_annual, data = sdata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2450.0  -173.0    -0.3   151.8  4964.6 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -29.30007   27.53434  -1.064    0.288    
## Rs_annual     1.03037    0.02951  34.921   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 372.7 on 821 degrees of freedom
## Multiple R-squared:  0.5976, Adjusted R-squared:  0.5971 
## F-statistic:  1219 on 1 and 821 DF,  p-value: < 2.2e-16
## 
## Tue Mar 26 18:09:53 2019  Plotting and saving model diagnostics...

## Tue Mar 26 18:09:53 2019  Plotting and saving model residuals...

## Tue Mar 26 18:09:53 2019  Saving outputs/3-modelresids.pdf

## Saving 7 x 5 in image

## Tue Mar 26 18:09:53 2019  Test H0 of intercept=0: p-value = 0.2875834
## Tue Mar 26 18:09:53 2019  Test H0 of slope=1: p-value = 0.3037032

## [1] 0.2875834

## [1] 0.3037032

Using MAT

## 
## Call:
## lm(formula = bahn ~ Rs_annual, data = sdata)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2356.27  -167.25    -1.34   142.82  2746.39 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -32.36603   24.27741  -1.333    0.183    
## Rs_annual     1.00324    0.02602  38.563   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 328.6 on 821 degrees of freedom
## Multiple R-squared:  0.6443, Adjusted R-squared:  0.6439 
## F-statistic:  1487 on 1 and 821 DF,  p-value: < 2.2e-16

## Tue Mar 26 18:09:54 2019  -------------------+++++-------------------
## Tue Mar 26 18:09:54 2019  How are Rs_annual and Rs_annual_bahn_Temp related?
## Tue Mar 26 18:09:54 2019  sdata rows = 823 cols = 143
## Tue Mar 26 18:09:54 2019  Model summary:
## 
## Call:
## lm(formula = temp ~ Rs_annual, data = sdata)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2356.27  -167.25    -1.34   142.82  2746.39 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -32.36603   24.27741  -1.333    0.183    
## Rs_annual     1.00324    0.02602  38.563   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 328.6 on 821 degrees of freedom
## Multiple R-squared:  0.6443, Adjusted R-squared:  0.6439 
## F-statistic:  1487 on 1 and 821 DF,  p-value: < 2.2e-16
## 
## Tue Mar 26 18:09:54 2019  Plotting and saving model diagnostics...

## Tue Mar 26 18:09:54 2019  Plotting and saving model residuals...

## Tue Mar 26 18:09:54 2019  Saving outputs/3-modelresids.pdf

## Saving 7 x 5 in image

## Tue Mar 26 18:09:54 2019  Test H0 of intercept=0: p-value = 0.1828444
## Tue Mar 26 18:09:54 2019  Test H0 of slope=1: p-value = 0.9008364

## [1] 0.1828444

## [1] 0.9008364

4.3 Main figure comparing Rs_annual with Rs_annual_bahn After filtration

4.3.1 Does the extreme values (>3000) pull the slope off 1?

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## Warning: Removed 510 rows containing missing values (geom_errorbarh).

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4.3.2 Does agriculture pull the slope off 1?

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## Warning: Removed 495 rows containing missing values (geom_errorbarh).

## Warning: Removed 16 rows containing missing values (geom_errorbarh).

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## Warning: Removed 16 rows containing missing values (geom_errorbarh).

4.3.3 Does wetland pull the slope off 1?

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## Warning: Removed 495 rows containing missing values (geom_errorbarh).

## Warning: Removed 8 rows containing missing values (geom_errorbarh).

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## Warning: Removed 8 rows containing missing values (geom_errorbarh).

4.3.4 Does desert pull the slope off 1?

• Only two data points available

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## Warning: Removed 495 rows containing missing values (geom_errorbarh).

## Warning in qt((1 - level)/2, df): NaNs produced

## Warning: Removed 2 rows containing missing values (geom_errorbarh).

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## Warning in qt((1 - level)/2, df): NaNs produced

## Warning in qt((1 - level)/2, df): Removed 2 rows containing missing values
## (geom_errorbarh).

4.3.5 Does Meas_method pull the slope off 1?

## Warning: Removed 454 rows containing missing values (geom_errorbarh).

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## Warning: Removed 454 rows containing missing values (geom_errorbarh).

## Warning: Removed 57 rows containing missing values (geom_errorbarh).

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## Warning: Removed 57 rows containing missing values (geom_errorbarh).

4.3.6 Does TAIR_LTM_dev () pull the slope off 1? – YES!!!!!

srdb_mat <- subset( srdb, TAIR_LTM_dev <= 2 )
srdb_mat2 <- subset( srdb, TAIR_dev <= 2 )

4.3.6 Does TAIR_dev () pull the slope off 1? – YES!!!!!

## Warning: Removed 187 rows containing missing values (geom_errorbarh).

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## Warning: Removed 187 rows containing missing values (geom_errorbarh).

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4.4 RA or RH dominated sites differ?

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4.5 Annual Rs or Ts coverage effect

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4.6 TAIR and precipitation variability affect?

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4.7 Does drought affect?

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4.8 test the relationship between Rs_annual and Rs_mat

## Tue Mar 26 18:10:10 2019  -------------------+++++-------------------
## Tue Mar 26 18:10:10 2019  Bahn relationship for these data:
## 
## Call:
## lm(formula = Rs_annual ~ Rs_TAIR, data = sdata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -746.75 -111.49  -39.62   86.43 1273.96 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  160.045     13.255   12.07   <2e-16 ***
## Rs_TAIR      344.963      5.926   58.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 194.7 on 821 degrees of freedom
## Multiple R-squared:  0.8049, Adjusted R-squared:  0.8047 
## F-statistic:  3388 on 1 and 821 DF,  p-value: < 2.2e-16

5. Monthly and daily results

6. More analysis in the future

1 T&Drought function (Maybe use PDSI) 2 seprete out Agriculture & Wetland 3 Using SD information with boosting? 4 Use Rs_mat predict Rh? 5 Use this approach estimate global Rs 6 Think about application

subtest_1(5227)

##        T     a      b c  d Model_output_units                   Model_type
## 451 14.2 0.573 0.0924 0 NA      umol CO2/m2/s Exponential, R=a exp(b(T-c))
##     Record_number Study_number Rs_annual Rs_annual_bahn Rs_TAIR_units
## 451          3430         5227    722.26       696.5665      1.524751